A Visualization Platform For Exploring Cooperation
Remy Cazabet

Hideaki Takeda

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-ku
Tokyo, japan

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-ku
Tokyo, japan

remy.cazabet@gmail.com

takeda@nii.ac.jp

ABSTRACT

1.1

In this paper, we present a platform designed to explore visually massive cooperation between individuals. With theincreasing importance of the Internet, new types of cooperation are becoming common, in which hundreds, thousands
or millions of individuals act together in interaction, andproduces content in a decentralized manner. As these processes are happening in real-time and without organization,individuals involved in them often do not have a clear visionof what is happening, or even which role they play in it. The
visualization we propose would allow users to take back thepower of understanding the processes to which they participate in. We combine time series visualization, together withcustom network visualization, in a way generic enough toadapt to many situations, while offering numerous possibilities.

Several visualizations have been proposed to understand complex systems and large data in general. We introduce themost closely related to our proposition.

1.

INTRODUCTION

Since the advent of the digital era, both the technical possibilities and the introduction of new behaviors have participated in the production of large databases storing tremendous amounts of varied information. Recent hot topics suchas Big Data, Complex Systems and Network Analysis havebeen stimulated by this new access to information. One
particular topic of interest is the study of how crowds are
involved in massive generation of content, whether it be on
Wikipedia, Twitter, Facebook, YouTube, or even throughthe publication of ever growing number of scientific publications. If these datasets are a stimulating opportunity, they
are also a challenge. While many research has been done onthese topics, we feel there is no simple, generic method to
explore this decentralized creation of content, and in particular its dynamic. The platform we propose is generic enoughto take input from many kinds of sources, such as scientificpublications, online social networks, and many others. The
platform is developed with internet based tools only, andcould therefore be adapted to provide a user-friendly interface to explore a large dataset of content creation available
on the internet.

Related Works

ThemeRiver [9] is probably the most famous of these. It
allows to represents the dynamic of topics in large collectionsof documents.
History flows [16]also focuses on dynamic aspects. It is atool to visualize cooperation and conflict between authors
in the process of collaboration, in particular on the web.
In the work by Rosvall et al.[12], alluvial diagrams are used
to represent the evolution of communities in networks, andis applied in particular for the visualization of the evolutionof research topics in science.
On a more static perspective, numerous tools, frameworks
and softwares have been proposed to represent networks inthe best possible way. We can cite some of them, among themost famous ones: Gephi[2], Cytoscape[13], Tulip [1].
Several works have also been done on the visualization of
dynamic networks; we can cite [3] as a reference on the domain.
The tools we have cited above are either specialized on thevisualization of longitudinal aspects, but without information on the internal structure, or, on the contrary, represent
this internal structure (network visualization), but only witha static point of view. Our platform is designed to encompass both aspects.

2.

MASS COOPERATION DATASETSIn order to illustrate the possibilities and possible practicalapplications of the tools presented in this paper, we appliedthem to three large datasets from different fields. In thissection, we will present briefly these datasets, and the typeof data we extract from them.
For a dataset to be visualized using our platform, it needs
to be composed of several productions, that we call Cooperative Productions (CPs). It can be a video, an article,
a website, a message, or any other item which can make areference and be referenced. These CPs are defined by thefollowing properties:• Name• Time of publication• Category (a chain of character, can be omitted)• List of references it makes to other CPs
Additionally, we need to group these CPs in Cooperation
processes. A cooperation process is a set of CPs corresponding to a same topic, a same goal, or any other wayof grouping them relevant to the studied dataset. In the following sections, we will detail these properties in 3 example
datasets.

2.1

Definition of a cooperation Process
We first counted for each tweet in our dataset the number oftime they were retweeted, following the method described in[4]. For all tweets retweeted more than 500 times, we collect
all the involved tweets and their information. Each of thesesets of tweet form a cooperation flow.
Definition of a CP
• Name : Retweeter’s name• Time : Time of the Retweet• Category : Distance in the follower network between
original author and retweeter

NicoNico

NicoNico, or Nico Nico Douga, is a Japanese video-sharing
platform, with functionalities similar to those of YouTube.
With officially more than 20 Million registered users, and being ranked among the top 15 most visited websites of Japan,
it is a major Web 2.0 platform. It is especially famous forthe important community of people cooperating in the creation of complex Music Videos centered on the character of
Hatsune Miku. Starting from an original song, many people
create videos based on it, with innovation such as dancing,singing, creating new graphics, etc. More information about
this character and phenomenon can be found in [8, 10, 6].
We use the dataset described in [7] which covers all 2,622,495
videos published on the network between January 2007 andDecember 2012.
Definition of a cooperation process
In NicoNico, tags are associated with videos. We automatically detect tags corresponding to songs with more than
500 related videos. These videos compose the cooperation
processes.
Definition of a CP
• Name : Name of the Video• Time : Upload time
• Category : extracted from keywords, examples are:
Dancing, Singing, 3D, Animation...

• References: a retweet
Statistics
45 cooperation processes corresponding to retweet chains aredetected, involving between 500 and 2100 tweets, with anaverage of 755 tweets.

2.3

Definition of a cooperation Process
As we lack topic information, we define a cooperation process for each article, with all other papers making a directreference to it composing the cooperation processes. Thisdefinition is not perfect, but, as we know that seminal papers tend to act as ”flags”, that must be cited by everyone
working on a specific topic, looking at all papers citing aseminal one is an approximation of a group of works in thesame topic. We filtered out all cooperation processes withless than 500 elements.
Definition of a CP
• Name : Publication Title• References: authors include references to other videos
in their comments.

• Time : Date of Publication• Category : Venue of publication

Statistics
We obtain 165 cooperation processes, composed by 500 to
7654 videos, with an average of 865 videos.

2.2

Twitter

Twitter is one of the most famous and largest Online Social Networks. In this paper, we consider the diffusion ofa particular tweet as our cooperation processes. We useda dataset covering the period between March 5, 2011 andMarch 24, and which covers most tweets published in Japan
during this period. Authors of this dataset claim to havevalidated that 80% to 90% of all published tweets appear in
their dataset. For more information, please refer to [15].

DBLP

Massive cooperation predates the apparition of the World
Wide Web. Thousands of researchers around the world cooperate to improve the global scientific knowledge. We use
as a dataset the DBLP database [11], and in particular theversion including links between papers, as described in [14].
This database is composed of 2,084,055 articles linked by
2,244,018 citations.

• References: a citation to another paper
Statistics
After filtering, we obtained 41 citation flows, composed of
between 500 and 3651 papers, with an average of 664 papers.

3.

DESCRIPTION OF THE PLATFORM

The platform we propose is composed of two parts: the timeseries visualization and the cooperation flow visualization.
The time series provides a global understanding of the different cooperation processes studied, together with global

Time Line view

Cooperation Flow

Temporal Cooperation Flow

• The presence of bursts at a particular location, or following a fix period• Differences between cooperation processes starting atdifferent times
We complete this tool with some metrics:

Figure 1: Schema of the possible navigation between thedisplays of Cooperation Explorer

3.1.2

indicators on them. In this view, only the global propertiesare represented, not the individual agents and their interactions. From this global view, it is then possible to select any
cooperation process and to visualize its inner details in thecooperation flow view.

LifespanFor each cooperative Process, we compute its lifespan, defined as the time between the first not null value of the timeseries to the last occurrence of 3 consecutive not null values.
This limit is arbitrary, but the objective is to give an end
to a time series, potentially infinite, as a new CPs can always occurs in the future. If these 3 non-null values are thelast 3 values of the time series, we consider the cooperation
process as ”still alive”. The distribution of the lifespans isdisplayed as a bar chart.

In this second tool, in which the details of the cooperation
is displayed, several options are possible such as positioning
according to time or to step of cooperation, selecting thenumber of nodes displayed, etc. The navigation between
these different displays in represented in Fig. 1

3.1

Temporal trends

When we are interested in a cooperation process, it is often useful to have first a global vision of it. We would liketo be able to answer general questions such as: when did
this process started? Is it already finished? Is it becoming
more or less popular? Are there some patterns in its popularity? These are the global properties of this particular
cooperation.

3.1.1

metrics and graphics

Normalized centroid
We compute the normalized centroid of each cooperative
flow. The centroid of the time series is the step such as
there is as many CPs before and after it. We normalize itby computing:N ormalizedCentroid =centroidT ime − birthT ime.
deathT ime − birthT ime

A normalized centroid inferior or superior to 0.5 reflect thefact that most of the CPs where produced in the beginning
or in the end of the lifespan of the cooperation process. The
distribution of the normalized centroid is displayed as a bar
chart.

Macro-level visualization: time series

The visualization we propose excludes the role of each specific element, to represents the process as a whole. To doso, we choose to transform our data in time series, as muchwork exists on the topic of time series analysis. For a given
dataset, we define a time step, which can be any period oftime (minute, day, year, etc.) and count the number of CPs
published for each category in each time step. For each Cooperation Flow, we obtain as many time series as there arecategories. We display them as a shape, as shown in Fig.
7. The shape is constructed as a cumulative area chart augmented with a mirror image of itself, to have a symmetricshape. The lecture of it is identical to a normal cumulative area chart. We choose this shape instead of a normal
cumulative area chart because we want to represent several of these shapes on a same plot with a single time axis.
Therefore, the shape is not framed by the axis, and whendisplayed on top of each other, it becomes more natural tohave a horizontally symmetrical shape, as represented on
Fig. 8. A similar observation has been done by the authorsof ThemeRiver [9].
By displaying several shapes on the same chart, we are able
to visually compare them. Examples of interesting observable facts include (but are not limited to):• The relative importance of different categories along
time

Figure 2: Visualization of periodic bursts in a temporal
trend. Detected bursts appear in translucent blue color. In
this case, we can observe yearly events.
Burst detection
Burst detection is a common problem on time series. A burst
is defined as a period of time during which the time series
reach temporarily exceptionally high values. In a cooperation flow, such a burst can typically appears in the beginning(initial burst), at a given moment, driven by internal events(new popular CP), or external factors. An interesting caseis when this external factor is not unique but periodic, typically daily or yearly events. We therefore implemented a
research of such periodic bursts. We implemented the burst
detection with a simple but effective technique, presented in[17]. We represent the bursting period with a translucent
color as seen in 2. We compute normalize burst positions in
a similar manner as we computed normalized centroid, andthe summary of the most common burst positions detected
is also represented as a bar chart.
We found 5 cooperation processes with periodic bursts in
the NicoNico dataset, and we checked that all of them cor-

responded to yearly events (songs about Christmas, Halloween, etc.).

3.2

Micro-level visualization

Whereas the time series visualization allow us to have aquick understanding of global properties, it is often useful tohave more insights in the details of what is happening insideeach cooperative topic. In this second display, we combine avisualization called cooperation flow together with some alternatives displays and indicators, each of them emphasizing
one aspect of the studied cooperative topic.
Original nodes (roots)
Level 0Level 1Figure 5: Example of a cooperation flow where the x position
represents time

3.2.2

Level 23.2.3
Time (for a same level)

Levels of cooperation

Figure 3: Mechanism of the cooperation flow visualization.
Quantity of undisplayed
references
RI-Torus
Valueof the node (from
data)

Node

Colors represent categories
of nodesFigure 4: Schema of the representation of a node

3.2.1

Temporal Cooperation Flow

One interesting property of this visualization is that nodes
situated on overlapping y values are necessarily ordered in achronological order from left to right. Therefore, it is possible to switch to a temporal representation without changing
the y position of nodes. This is illustrated on figure 5.

Cooperation FlowTo represent the details of the process of cooperation, we use
a type of visualization described in [5]. This visualization,
called Cooperation flow, allows us to represent in a single
visualization the key points of the details of the process. Its
mechanism is represented in Fig.3. The idea is that, throughthe interface, we specify the maximum number of nodes that
we want to display, n. An algorithm compute which are then most important elements for the cooperation in the current
process. These nodes are then displayed, together with theirrelations, as a network organized by steps of cooperation.
More formally, the step of a node is defined as the length of
the shortest path between this node and a root, that is tosay a node without any reference to other nodes. The nodes
which are not considered important enough to be displayed
are, however, not simply omitted. By using a feature called
Reuse Indicator Torus (RI-Torus), a summary of these nodes
appears around their last displayed ancestor, as summarized
in Fig. 4.

Complementary visualizations and metrics

We added to this visualization a set of informative visualization and metrics, each of them focusing on a specific aspectof the cooperation. These tools are based on the same dataas the cooperation flow visualization. All of these tools are
not affected by the selection of nodes we make for the flow
visualization, they are based on all available information.
Impact of main CPs
We observed that one characteristic which can vary greatly
between cooperation flows is the importance taken by themost important productions. In some cases, a single production, or a small subset of them, can generate most of theCPs, that is, most of the CPs will directly reference it as aunique source, either during the whole lifetime of the flow,or just during a given period. To study this, we propose
a visualization in stacked area of the impact along time of
the top 5 nodes, toped by the impact of all remaining nodes(Fig. 6). The lifetime of the flow is split in 10 sections. The
impact of a given CP during a given section is computed as
the number of CPs published during this period that reference it. We use a black and white scale to avoid confusion
with the categories of CPs, already represented by colors.
Together with this visualization, we propose a metric tomeasure this effect, called CSC, for Cooperation Source Concentration.
Pv∈T op1 |{u : (u, v) ∈ E}|
CSC =|V | − 1
Where T op1 is the set of the 1% nodes with the highest indegree. This metric vary between lim0 and 1, where lim0
is the case where all nodes have the same in-degree, and 1
is reached when all nodes are successors of a single original
source. (star-like network) We give the average values of
CSC for our 3 datasets in Table 1. We can observe large
differences, with NicoNico having the strongest CSC andTwitter the lowest.
Sustainability of the cooperation
We observed that in some cooperation flow, there is notmuch cooperation after the first few levels -the number of
CP by level follows a fast shrinking trend- while, in others,
it is not the case. This might reflect the ability to renew theNicoNico
0.92

Average CSC

Twitter
0.21

DBLP
0.65

Table 1: Average value of CSC by dataset. CSC represents
how important is the role of the top 1% users in the cooperation.
Node:nb CPsnb CPsFigure 7: Visualization of the temporal trend of a cooperation flow

Time (normalized)

220
200
180
160
140
120
100
80
60
40
20

4.

4.1

(a) Impact of the main CPs along
Leveltime, in a particular cooperation
flow. In this example, we can see (b) Sustainability of the cooperathat 2 or 3 nodes are the source tion. In this case, we can observeof most cooperation. For example, a shift in the categories of CPs as
the increasing number of videos in with progress in the levels of coopperiod 6 is mainly due to the pop- eration. This pattern is common
ularity of a single node.
on NicoNico.

Figure 6: Additional analysis tools

interest in the trend by new CPs. We propose a visualizationof this effect by a stacked bar chart graph (Fig. 6). Eachbar represent a level, and we simply count the number ofvideos of each type published in each level. Together withthe general trend, this chart allows to see a change in the
categories correlated with the level. The color used for the
categories are coherent with the ones used in the cooperation
flow chart.
The indicator we propose to summarize this chart is SC,
Sustainability of Cooperation. It is defined as the averageof the variations of the number of CPs between successive
levels, pondered by the number of CPs in the first of thetwo:Pnl−1 nbCP (i+1)∗ (nbCP (i + 1) + nbCP (i))
i=1
nbCP (i)SC =Pnl−2
nbCP (1) + 2 i=2 nbCP (i) + nbCP (nl − 1)with nbCP (i) the number of CPs at level i, and nl the number of levels. SC=0 if there is no production after the firstlevel. SC >1 if the number of CPs tends to grow with eachlevel. The lower the SC value, the less CPs tend to generate
new cooperation. In Table 2, we represent the average valueof SC for our datasets.

Average SC

NicoNico
0.23

Twitter
0.39

EXAMPLE VISUALIZATIONSIn this section, we briefly present some examples to show
the interest of our visualization.

DBLP
0.44

Table 2: Average value of SC by dataset. SC represents
the average ratio between the number of videos publishedat step i and i + 1.

Temporal trends

Our visualization allows to display on a same timeline thetime series of several temporal trends. We can therefore
compare them, observe some typical behaviors or spot outliers. In Fig. 8, we show an example of this view on our
example dataset. We can observe very different properties.
For instance, in Twitter, we see a typical bursting behavior, followed by a rapid decay. Most of the productions, i.e.,
retweets, occur in the beginning. On Nico Nico, the trends
are more long lasting, bursts are not as important. People continue to publish videos at the same rate for years.
Finally, in the citation dataset, we observed more varied
patterns, and even some ”increasing” trends, for which thenumber of papers published increase from years to years.

4.2
4.2.1

Cooperation flows
Deep study of one dataset: NicoNico

NicoNico is the richest and the most complex of our datasets.
In fig. 9, we show 2 typical flow from this network. We canmake the following observations, also valid on most otherflows:
1. There is only one original source, and most of the cooperation is made directly from this source, as we canjudge by the large RI-Torus
2. Most important nodes for the collaboration are on thefirst level, they directly reference the original node only
3. The cooperation is more wide than deep, there is notmuch cooperation at a level greater than 3.
4. Although many categories (colors) are present, eachnode seems to generate a specialized cooperation: RITorus are mostly of a single color, not always the same.
5. There is no strong correlation between the number ofview of a video (area of inner circle) and its capacity
to generate cooperative behavior (torus area)

4.2.2

Comparison of datasets

In fig. 10, we present two visualizations typical of the otherdatasets. We can immediately spot some differences. In
the tweet dataset, cooperation is deeper, and we tend to
see the formation of chains, long but without many bifurcations. More important nodes are not necessarily situated(a) Twitter
dataset(b) Scientific dataset(c) Nico Nico Dataset

Figure 8: Examples of temporal trends

Figure 9: Examples of typical cooperation flow in NicoNico(a) Twitter dataset(b) DBLP dataset

Figure 10: Examples of typical cooperation flow in Twitter and DBLP

at the first step, but can occur deeper. There seems to bea stronger relation between the popularity of the node andits role in the cooperation. There is not a single source.
In the citation dataset, we immediately spot a large number of nodes making references to several others. These
nodes with many references are important in the cooperation. Nodes at a deep level seem to generate as much cooperation as those in the first levels. There also seems to be alesser concentration in the cooperation generation: a larger
fraction of nodes are referenced by other important nodes,
and the gap is less important between the top influential
nodes and the ordinary ones. Exploring in further details
the properties of the different datasets is beyond the scopeof this paper.

5.

CONCLUSION

In this paper, we have presented a platform to explore mass
cooperation, and a set of tools to explore different aspectsof this type of cooperation. Our conception of such visualization was driven by our previous experiences in the exploration of large datasets formed by cooperation, and the
difficulties encountered to understand the underlying mechanisms.
We also presented some complementary visualizations andmetrics that focus on several aspects of the data, with different granularities, and can also help to apprehend it.
In the future, we hope that other researchers will use thisplatform and help to improve it, either by their remarks orextending the possibilities. In this prospect, we release its
source code, altogether with an interactive online version,
so as interested researchers could work with it as easily as
possible. In particular, it could be interesting to add metrics
and statistics, such as a one could choose the more interesting indicators in his case. The source code and browsable
example is available on the website of the first author.
Another future possibility is to propose Internet applications
based on this visualization to the destination of final end
users. For example, one can think of a plug-in for Google
Scholar allowing to browse research topics.

6.

ACKNOWLEDGMENTS

We thank Fujio Toriumi for collecting the Twitter dataset,and allowing us to make use of it in this work.

7.

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